Spaces:
Runtime error
Runtime error
File size: 7,702 Bytes
983684c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
date: 2020.1.5
note: code is heavily borrowed from
https://github.com/NVlabs/ffhq-dataset
http://dlib.net/face_landmark_detection.py.html
requirements:
apt install cmake
conda install Pillow numpy scipy
pip install dlib
# download face landmark model from:
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""
from argparse import ArgumentParser
import time
import numpy as np
import PIL
import PIL.Image
import os
import scipy
import scipy.ndimage
import dlib
import multiprocessing as mp
import math
#from configs.paths_config import model_paths
SHAPE_PREDICTOR_PATH = 'shape_predictor_68_face_landmarks.dat'#model_paths["shape_predictor"]
def get_landmark(filepath, predictor):
"""get landmark with dlib
:return: np.array shape=(68, 2)
"""
detector = dlib.get_frontal_face_detector()
if type(filepath) == str:
img = dlib.load_rgb_image(filepath)
else:
img = filepath
dets = detector(img, 1)
if len(dets) == 0:
print('Error: no face detected!')
return None
shape = None
for k, d in enumerate(dets):
shape = predictor(img, d)
if shape is None:
print('Error: No face detected! If you are sure there are faces in your input, you may rerun the code several times until the face is detected. Sometimes the detector is unstable.')
t = list(shape.parts())
a = []
for tt in t:
a.append([tt.x, tt.y])
lm = np.array(a)
return lm
def align_face(filepath, predictor):
"""
:param filepath: str
:return: PIL Image
"""
lm = get_landmark(filepath, predictor)
if lm is None:
return None
lm_chin = lm[0: 17] # left-right
lm_eyebrow_left = lm[17: 22] # left-right
lm_eyebrow_right = lm[22: 27] # left-right
lm_nose = lm[27: 31] # top-down
lm_nostrils = lm[31: 36] # top-down
lm_eye_left = lm[36: 42] # left-clockwise
lm_eye_right = lm[42: 48] # left-clockwise
lm_mouth_outer = lm[48: 60] # left-clockwise
lm_mouth_inner = lm[60: 68] # left-clockwise
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# read image
if type(filepath) == str:
img = PIL.Image.open(filepath)
else:
img = PIL.Image.fromarray(filepath)
output_size = 256
transform_size = 256
enable_padding = True
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, PIL.Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Pad.
pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
max(pad[3] - img.size[1] + border, 0))
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
quad += pad[:2]
# Transform.
img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
# Save aligned image.
return img
def chunks(lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i:i + n]
def extract_on_paths(file_paths):
predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
pid = mp.current_process().name
print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
tot_count = len(file_paths)
count = 0
for file_path, res_path in file_paths:
count += 1
if count % 100 == 0:
print('{} done with {}/{}'.format(pid, count, tot_count))
try:
res = align_face(file_path, predictor)
res = res.convert('RGB')
os.makedirs(os.path.dirname(res_path), exist_ok=True)
res.save(res_path)
except Exception:
continue
print('\tDone!')
def parse_args():
parser = ArgumentParser(add_help=False)
parser.add_argument('--num_threads', type=int, default=1)
parser.add_argument('--root_path', type=str, default='')
args = parser.parse_args()
return args
def run(args):
root_path = args.root_path
out_crops_path = root_path + '_crops'
if not os.path.exists(out_crops_path):
os.makedirs(out_crops_path, exist_ok=True)
file_paths = []
for root, dirs, files in os.walk(root_path):
for file in files:
file_path = os.path.join(root, file)
fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
continue
file_paths.append((file_path, res_path))
file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
print(len(file_chunks))
pool = mp.Pool(args.num_threads)
print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
tic = time.time()
pool.map(extract_on_paths, file_chunks)
toc = time.time()
print('Mischief managed in {}s'.format(toc - tic))
if __name__ == '__main__':
args = parse_args()
run(args)
|